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Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Publication date29/01/2020
Host publicationDeep Biometrics
EditorsRichard Jiang, Chang-Tsun Li, Danny Crookes, Weizhi Meng, Christophe Rosenberger
Place of PublicationCham
Number of pages13
ISBN (Electronic)9783030325831
ISBN (Print)9783030325824
<mark>Original language</mark>English

Publication series

NameUnsupervised and Semi-Supervised Learning
ISSN (Print)2522-848X
ISSN (Electronic)2522-8498


While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.